统一软聚类工具包，让各类聚类方法一键调用
SCPP: A Unified Python Library for Soft Clustering
- 提供统一接口，兼容scikit-learn标准流程
- 集成40种软聚类算法，支持完整评测体系
- 适合需要快速对比和扩展聚类方法的研究者
本文提出SCPP（Soft Clustering Python Package），一个开源的软聚类Python框架。该框架建立标准化、与scikit-learn兼容的估计器接口，统一了模糊聚类、概率聚类、图模型、矩阵分解及深度学习等异构软聚类方法的训练、预测、成员隶属表示、评估与基准测试流程。目前框架集成40个代表性算法，并包含涵盖多个数据集、聚类质量指标以及标准化运行时间、内存占用与可扩展性评估的完整基准测试体系。SCPP还提供详尽文档、实用示例、自动化测试及与科学计算Python生态的无缝集成，支持可复现实验和新算法的便捷扩展。源代码公开于https://github.com/soft-clustering/soft-clustering。
原文摘要 · Abstract (English)
In this paper, we present SCPP (Soft Clustering Python Package), an open-source Python framework for soft clustering. SCPP establishes a canonical, scikit-learn-compatible estimator interface that standardizes model training, prediction, membership representation, evaluation, and benchmarking across heterogeneous soft clustering methods, including fuzzy, probabilistic, graph-based, matrix factorization, and deep learning methods. The framework currently integrates 40 representative algorithms together with a comprehensive benchmarking comprising datasets, clustering quality metrics, and standardized runtime, memory, and scalability evaluation. SCPP further provides extensive documentation, practical examples, automated testing, and seamless integration with the scientific Python ecosystem, enabling reproducible experimentation and straightforward extension with new algorithms. The source code is publicly available at https://github.com/soft-clustering/soft-clustering.
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